Abstract

Aiming at the restoration of degraded social network services media, this paper proposed a novel multi-prior models collaboration framework for image restoration with big data. Different from the traditional non-reference media restoration strategies, a big reference image set is adopted to provide the references and predictions of different popular prior models and accordingly guide the further prior collaboration. With these cues, the collaboration of multi-prior models is mathematically formulated as a ridge regression problem in this paper. Due to the computation complexity of dealing big reference data, scatter-matrix-based KRR is proposed which can achieve high accuracy and low complexity in big data related decision making task. Specifically, an iterative pursuit is proposed to obtain further refined and robust estimation. Five popular prior methods are applied to evaluate the effectiveness of the proposed multi-prior models collaboration. Compared with the traditional restoration strategies, the proposed framework improves the restoration performance significantly and provides a reasonable method for the relative exploration of big data driven decision making.

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Acknowledgements

We would like to thank the authors of [2, 3, 12, 34] and [33] for kindly providing their codes. This work was supported in part by the National Science Council of the Republic of China under Grant No. 103-2917-I-564-058, the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2061978), the National Natural Science Foundation of China under Grant No. 61272386 and 61100096.